Theoretical and Applied Chemometrics Laboratory, Chemistry Institute, University of Campinas, Campinas, SP, Brazil.
Talanta. 2011 Feb 15;83(5):1352-8. doi: 10.1016/j.talanta.2010.11.001. Epub 2010 Nov 10.
Mathematical models based on chemometric analyses of the coffee beverage sensory data and NIR spectra of 51 Arabica roasted coffee samples were generated aiming to predict the scores of acidity, bitterness, flavour, cleanliness, body and overall quality of coffee beverage. Partial least squares (PLS) were used to construct the models. The ordered predictor selection (OPS) algorithm was applied to select the wavelengths for the regression model of each sensory attribute in order to take only significant regions into account. The regions of the spectrum defined as important for sensory quality were closely related to the NIR spectra of pure caffeine, trigonelline, 5-caffeoylquinic acid, cellulose, coffee lipids, sucrose and casein. The NIR analyses sustained that the relationship between the sensory characteristics of the beverage and the chemical composition of the roasted grain were as listed below: 1 - the lipids and proteins were closely related to the attribute body; 2 - the caffeine and chlorogenic acids were related to bitterness; 3 - the chlorogenic acids were related to acidity and flavour; 4 - the cleanliness and overall quality were related to caffeine, trigonelline, chlorogenic acid, polysaccharides, sucrose and protein.
基于化学计量学分析的咖啡饮料感官数据和 51 个阿拉比卡烘焙咖啡豆近红外光谱的数学模型被建立，旨在预测咖啡饮料的酸度、苦味、风味、清洁度、口感和整体质量评分。偏最小二乘法（PLS）被用于构建模型。有序预测选择（OPS）算法被应用于选择每个感官属性回归模型的波长，以便仅考虑重要区域。将光谱区域定义为与感官质量相关的区域与纯咖啡因、葫芦巴碱、5-咖啡酰奎宁酸、纤维素、咖啡脂类、蔗糖和酪蛋白的近红外光谱密切相关。近红外分析表明，饮料的感官特征与烘焙谷物的化学成分之间的关系如下：1 - 脂质和蛋白质与口感密切相关；2 - 咖啡因和绿原酸与苦味有关；3 - 绿原酸与酸度和风味有关；4 - 清洁度和整体质量与咖啡因、葫芦巴碱、绿原酸、多糖、蔗糖和蛋白质有关。
